Reading tea leaves worldwide: Decoupled drivers of initial litter decomposition mass-loss rate and stabilization.

The breakdown of plant material fuels soil functioning and biodiversity. Currently, process understanding of global decomposition patterns and the drivers of such patterns are hampered by the lack of coherent large-scale datasets. We buried 36,000 individual litterbags (tea bags) worldwide and found an overall negative correlation between initial mass-loss rates and stabilization factors of plant-derived carbon, using the Tea Bag Index (TBI). The stabilization factor quantifies the degree to which easy-to-degrade components accumulate during early-stage decomposition (e.g. by environmental limitations). However, agriculture and an interaction between moisture and temperature led to a decoupling between initial mass-loss rates and stabilization, notably in colder locations. Using TBI improved mass-loss estimates of natural litter compared to models that ignored stabilization. Ignoring the transformation of dead plant material to more recalcitrant substances during early-stage decomposition, and the environmental control of this transformation, could overestimate carbon losses during early decomposition in carbon cycle models.

Towards a toolkit for global insect biodiversity monitoring.

Insects are the most diverse group of animals on Earth, yet our knowledge of their diversity, ecology and population trends remains abysmally poor. Four major technological approaches are coming to fruition for use in insect monitoring and ecological research-molecular methods, computer vision, autonomous acoustic monitoring and radar-based remote sensing-each of which has seen major advances over the past years. Together, they have the potential to revolutionize insect ecology, and to make all-taxa, fine-grained insect monitoring feasible across the globe. So far, advances within and among technologies have largely taken place in isolation, and parallel efforts among projects have led to redundancy and a methodological sprawl; yet, given the commonalities in their goals and approaches, increased collaboration among projects and integration across technologies could provide unprecedented improvements in taxonomic and spatio-temporal resolution and coverage. This theme issue showcases recent developments and state-of-the-art applications of these technologies, and outlines the way forward regarding data processing, cost-effectiveness, meaningful trend analysis, technological integration and open data requirements. Together, these papers set the stage for the future of automated insect monitoring. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Lidar as a potential tool for monitoring migratory insects.

The seasonal migrations of insects involve a substantial displacement of biomass with significant ecological and economic consequences for regions of departure and arrival. Remote sensors have played a pivotal role in revealing the magnitude and general direction of bioflows above 150 m. Nevertheless, the takeoff and descent activity of insects below this height is poorly understood. Our lidar observations elucidate the low-height dusk movements and detailed information of insects in southern Sweden from May to July, during the yearly northward migration period. Importantly, by filtering out moths from other insects based on optical information and wingbeat frequency, we have introduced a promising new method to monitor the flight activities of nocturnal moths near the ground, many of which participate in migration through the area. Lidar thus holds the potential to enhance the scientific understanding of insect migratory behavior and improve pest control strategies.

Little directional change in the timing of Arctic spring phenology over the past 25 years.

With the global change in climate, the Arctic has been pinpointed as the region experiencing the fastest rates of change. As a result, Arctic biological responses-such as shifts in phenology-are expected to outpace those at lower latitudes. 15 years ago, a decade-long dataset from Zackenberg in High Arctic Greenland revealed rapid rates of phenological change.1 To explore how the timing of spring phenology has developed since, we revisit the Zackenberg time series on flowering plants, arthropods, and birds. Drawing on the full 25-year period of 1996-2020, we find little directional change in the timing of events despite ongoing climatic change. We attribute this finding to a shift in the temporal patterns of climate conditions, from previous directional change to current high inter-annual variability. Additionally, some taxa appear to have reached the limits of their phenological responses, resulting in a leveling off in their phenological responses in warm years. Our findings demonstrate the importance of long-term monitoring of taxa from across trophic levels within the community, allowing for detecting shifts in sensitivities and responses and thus for updated inference in the light of added information.

Climate Change Helps Polar Invasives Establish and Flourish: Evidence from Long-Term Monitoring of the Blowfly Calliphora vicina.

The isolated sub-Antarctic islands are of major ecological interest because of their unique species diversity and long history of limited human disturbance. However, since the presence of Europeans, these islands and their sensitive biota have been under increasing pressure due to human activity and associated biological invasions. In such delicate ecosystems, biological invasions are an exceptional threat that may be further amplified by climate change. We examined the invasion trajectory of the blowfly Calliphora vicina (Robineau-Desvoidy 1830). First introduced in the sub-Antarctic Kerguelen Islands in the 1970s, it is thought to have persisted only in sheltered microclimates for several decades. Here, we show that, in recent decades, C. vicina has been able to establish itself more widely. We combine experimental thermal developmental data with long-term ecological and meteorological monitoring to address whether warming conditions help explain its current success and dynamics in the eastern Kerguelen Islands. We found that warming temperatures and accumulated degree days could explain the species' phenological and long-term invasion dynamics, indicating that climate change has likely assisted its establishment. This study represents a unique long-term view of a polar invader and stresses the rapidly increasing susceptibility of cold regions to invasion under climate change.

Towards the fully automated monitoring of ecological communities.

High-resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real-time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components-for example, individual behaviours and traits, and species abundance and distribution-is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high-throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high-resolution, multidimensional and standardised data across complex ecologies.

Integrating natural gradients, experiments, and statistical modeling in a distributed network experiment: An example from the WaRM Network.

A growing body of work examines the direct and indirect effects of climate change on ecosystems, typically by using manipulative experiments at a single site or performing meta-analyses across many independent experiments. However, results from single-site studies tend to have limited generality. Although meta-analytic approaches can help overcome this by exploring trends across sites, the inherent limitations in combining disparate datasets from independent approaches remain a major challenge. In this paper, we present a globally distributed experimental network that can be used to disentangle the direct and indirect effects of climate change. We discuss how natural gradients, experimental approaches, and statistical techniques can be combined to best inform predictions about responses to climate change, and we present a globally distributed experiment that utilizes natural environmental gradients to better understand long-term community and ecosystem responses to environmental change. The warming and (species) removal in mountains (WaRM) network employs experimental warming and plant species removals at high- and low-elevation sites in a factorial design to examine the combined and relative effects of climatic warming and the loss of dominant species on community structure and ecosystem function, both above- and belowground. The experimental design of the network allows for increasingly common statistical approaches to further elucidate the direct and indirect effects of warming. We argue that combining ecological observations and experiments along gradients is a powerful approach to make stronger predictions of how ecosystems will function in a warming world as species are lost, or gained, in local communities.

Accurate image-based identification of macroinvertebrate specimens using deep learning-How much training data is needed?

Image-based methods for species identification offer cost-efficient solutions for biomonitoring. This is particularly relevant for invertebrate studies, where bulk samples often represent insurmountable workloads for sorting, identifying, and counting individual specimens. On the other hand, image-based classification using deep learning tools have strict requirements for the amount of training data, which is often a limiting factor. Here, we examine how classification accuracy increases with the amount of training data using the BIODISCOVER imaging system constructed for image-based classification and biomass estimation of invertebrate specimens. We use a balanced dataset of 60 specimens of each of 16 taxa of freshwater macroinvertebrates to systematically quantify how classification performance of a convolutional neural network (CNN) increases for individual taxa and the overall community as the number of specimens used for training is increased. We show a striking 99.2% classification accuracy when the CNN (EfficientNet-B6) is trained on 50 specimens of each taxon, and also how the lower classification accuracy of models trained on less data is particularly evident for morphologically similar species placed within the same taxonomic order. Even with as little as 15 specimens used for training, classification accuracy reached 97%. Our results add to a recent body of literature showing the huge potential of image-based methods and deep learning for specimen-based research, and furthermore offers a perspective to future automatized approaches for deriving ecological data from bulk arthropod samples.

Emerging technologies revolutionise insect ecology and monitoring.

Insects are the most diverse group of animals on Earth, but their small size and high diversity have always made them challenging to study. Recent technological advances have the potential to revolutionise insect ecology and monitoring. We describe the state of the art of four technologies (computer vision, acoustic monitoring, radar, and molecular methods), and assess their advantages, current limitations, and future potential. We discuss how these technologies can adhere to modern standards of data curation and transparency, their implications for citizen science, and their potential for integration among different monitoring programmes and technologies. We argue that they provide unprecedented possibilities for insect ecology and monitoring, but it will be important to foster international standards via collaboration.

Moths complement bumblebee pollination of red clover: a case for day-and-night insect surveillance.

Recent decades have seen a surge in awareness about insect pollinator declines. Social bees receive the most attention, but most flower-visiting species are lesser known, non-bee insects. Nocturnal flower visitors, e.g. moths, are especially difficult to observe and largely ignored in pollination studies. Clearly, achieving balanced monitoring of all pollinator taxa represents a major scientific challenge. Here, we use time-lapse cameras for season-wide, day-and-night pollinator surveillance of Trifolium pratense (L.; red clover) in an alpine grassland. We reveal the first evidence to suggest that moths, mainly Noctua pronuba (L.; large yellow underwing), pollinate this important wildflower and forage crop, providing 34% of visits (bumblebees: 61%). This is a remarkable finding; moths have received no recognition throughout a century of T. pratense pollinator research. We conclude that despite a non-negligible frequency and duration of nocturnal flower visits, nocturnal pollinators of T. pratense have been systematically overlooked. We further show how the relationship between visitation and seed set may only become clear after accounting for moth visits. As such, population trends in moths, as well as bees, could profoundly affect T. pratense seed yield. Ultimately, camera surveillance gives fair representation to non-bee pollinators and lays a foundation for automated monitoring of species interactions in future.

Impacts of elevation on plant traits and volatile organic compound emissions in deciduous tundra shrubs.

The northernmost regions of our planet experience twice the rate of climate warming compared to the global average. Despite the currently low air temperatures, tundra shrubs are known to exhibit high leaf temperatures and are increasing in height due to warming, but it is unclear how the increase in height will affect the leaf temperature. To study how temperature, soil moisture, and changes in light availability influence the physiology and emissions of climate-relevant volatile organic compounds (VOCs), we conducted a study on two common deciduous tundra shrubs, Salix glauca (separating males and females for potential effects of plant sex) and Betula glandulosa, at two elevations in South Greenland. Low-elevation Salix shrubs were 45% taller, but had 37% lower rates of net CO2 assimilation and 63% lower rates of isoprene emission compared to high-elevation shrubs. Betula shrubs showed 40% higher stomatal conductance and 24% higher glandular trichome density, in the low-elevation valley, compared to those from the high-elevation mountain slope. Betula green leaf volatile emissions were 235% higher at high elevation compared to low elevation. Male Salix showed a distinct VOC blend and emitted 55% more oxygenated VOCs, compared to females, possibly due to plant defense mechanisms. In our light response curves, isoprene emissions increased linearly with light intensity, potentially indicating adaptation to strong light. Leaf temperature decreased with increasing Salix height, at 4 °C m-1, which can have implications for plant physiology. However, no similar relationship was observed for B. glandulosa. Our results highlight that tundra shrub traits and VOC emissions are sensitive to temperature and light, but that local variations in soil moisture strongly interact with temperature and light responses. Our results suggest that effects of climate warming, alone, poorly predict the actual plant responses in tundra vegetation.

Thermal acclimation has limited effect on the thermal tolerances of summer-collected Arctic and sub-Arctic wolf spiders.

High-latitude ectotherms contend with large daily and seasonal temperature variation. Summer-collected wolf spiders (Araneae; Lycosidae) from sub-Arctic and Arctic habitats have been previously documented as having low temperature tolerance insufficient for surviving year-round in their habitat. We tested two competing hypotheses: that they would have broad thermal breadth, or that they would use plasticity to extend the range of their thermal performance. We collected Pardosa moesta and P. lapponica from the Yukon Territory, Canada, P. furcifera, P. groenlandica, and P. hyperborea from southern Greenland, and P. hyperborea from sub-Arctic Norway, and acclimated them to warm (12 or 20 °C) or cool (4 °C) conditions under constant light for one week. We measured critical thermal minimum (CTmin) or supercooling point (SCP) as a measure of lower thermal limit, and critical thermal maximum (CTmax) as a measure of upper thermal limit. We found relatively little impact of acclimation on thermal limits, and some counterintuitive responses; for example, warm acclimation decreased the SCP and/or cool acclimation increased the CTmax in several cases. Together, this meant that acclimation did not appear to modify the thermal breadth, which supports our first hypothesis, but allows us to reject the hypothesis that spiders use plasticity to fine-tune their thermal physiology, at least in the summer. We note that we still cannot explain how these spiders withstand the very cold winters, and speculate that there are acclimatisation cues or processes that we were unable to capture in our study.

Deep learning and computer vision will transform entomology.

Most animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is sparse. Insect populations are challenging to study, and most monitoring methods are labor intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors can effectively, continuously, and noninvasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the laboratory. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behavior, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. We present examples of sensor-based monitoring of insects. We show how deep learning tools can be applied to exceptionally large datasets to derive ecological information and discuss the challenges that lie ahead for the implementation of such solutions in entomology. We identify four focal areas, which will facilitate this transformation: 1) validation of image-based taxonomic identification; 2) generation of sufficient training data; 3) development of public, curated reference databases; and 4) solutions to integrate deep learning and molecular tools.

Nonlinear trends in abundance and diversity and complex responses to climate change in Arctic arthropods.

Time series data on arthropod populations are critical for understanding the magnitude, direction, and drivers of change. However, most arthropod monitoring programs are short-lived and restricted in taxonomic resolution. Monitoring data from the Arctic are especially underrepresented, yet critical to uncovering and understanding some of the earliest biological responses to rapid environmental change. Clear imprints of climate on the behavior and life history of some Arctic arthropods have been demonstrated, but a synthesis of population-level abundance changes across taxa is lacking. We utilized 24 y of abundance data from Zackenberg in High-Arctic Greenland to assess trends in abundance and diversity and identify potential climatic drivers of abundance changes. Unlike findings from temperate systems, we found a nonlinear pattern, with total arthropod abundance gradually declining during 1996 to 2014, followed by a sharp increase. Family-level diversity showed the opposite pattern, suggesting increasing dominance of a small number of taxa. Total abundance masked more complicated trajectories of family-level abundance, which also frequently varied among habitats. Contrary to expectation in this extreme polar environment, winter and fall conditions and positive density-dependent feedbacks were more common determinants of arthropod dynamics than summer temperature. Together, these data highlight the complexity of characterizing climate change responses even in relatively simple Arctic food webs. Our results underscore the need for data reporting beyond overall trends in biomass or abundance and for including basic research on life history and ecology to achieve a more nuanced understanding of the sensitivity of Arctic and other arthropods to global changes.

Parasitoids indicate major climate-induced shifts in arctic communities.

Climatic impacts are especially pronounced in the Arctic, which as a region is warming twice as fast as the rest of the globe. Here, we investigate how mean climatic conditions and rates of climatic change impact parasitoid insect communities in 16 localities across the Arctic. We focus on parasitoids in a widespread habitat, Dryas heathlands, and describe parasitoid community composition in terms of larval host use (i.e., parasitoid use of herbivorous Lepidoptera vs. pollinating Diptera) and functional groups differing in their closeness of host associations (koinobionts vs. idiobionts). Of the latter, we expect idiobionts-as being less fine-tuned to host development-to be generally less tolerant to cold temperatures, since they are confined to attacking hosts pupating and overwintering in relatively exposed locations. To further test our findings, we assess whether similar climatic variables are associated with host abundances in a 22 year time series from Northeast Greenland. We find sites which have experienced a temperature rise in summer while retaining cold winters to be dominated by parasitoids of Lepidoptera, with the reverse being true for the parasitoids of Diptera. The rate of summer temperature rise is further associated with higher levels of herbivory, suggesting higher availability of lepidopteran hosts and changes in ecosystem functioning. We also detect a matching signal over time, as higher summer temperatures, coupled with cold early winter soils, are related to high herbivory by lepidopteran larvae, and to declines in the abundance of dipteran pollinators. Collectively, our results suggest that in parts of the warming Arctic, Dryas is being simultaneously exposed to increased herbivory and reduced pollination. Our findings point to potential drastic and rapid consequences of climate change on multitrophic-level community structure and on ecosystem functioning and highlight the value of collaborative, systematic sampling effort.

Arthropods and climate change - arctic challenges and opportunities.

The harsh climate, limited human infrastructures, and basic autecological knowledge gaps represent substantial challenges for studying arthropods in the Arctic. At the same time, rapid climate change, low species diversity, and strong collaborative networks provide unique and underexploited Arctic opportunities for understanding species responses to environmental change and testing ecological theory. Here, I provide an overview of individual, population, and ecosystem level responses to climate change in Arctic arthropods. I focus on thermal performance, life history variation, population dynamics, community composition, diversity, and biotic interactions. The species-poor Arctic represents a unique opportunity for testing novel, automated arthropod monitoring methods. The Arctic can also potentially provide insights to further understand and mitigate the effects of climate change on arthropods worldwide.

Earlier springs enable high-Arctic wolf spiders to produce a second clutch.

Spiders at southern latitudes commonly produce multiple clutches, but this has not been observed at high latitudes where activity seasons are much shorter. Yet the timing of snowmelt is advancing in the Arctic, which may allow some species to produce an additional clutch. To determine if this is already happening, we used specimens of the wolf spider Pardosa glacialis caught by pitfall traps from the long-term (1996-2014) monitoring programme at Zackenberg, NE Greenland. We dissected individual egg sacs and counted the number of eggs and partially developed juveniles, and measured carapace width of the mothers. Upon the discovery of a bimodal frequency distribution of clutch sizes, as is typical for wolf spiders at lower latitudes producing a second clutch, we assigned egg sacs to being a first or second clutch depending on clutch size. We tested whether the median capture date differed among first and second clutches, whether clutch size was correlated to female size, and whether the proportion of second clutches produced within a season was related to climate. We found that assigned second clutches appeared significantly later in the season than first clutches. In years with earlier snowmelt, first clutches occurred earlier and the proportion of second clutches produced was larger. Likely, females produce their first clutch earlier in those years which allow them time to produce another clutch. Clutch size for first clutches was correlated to female size, while this was not the case for second clutches. Our results provide the first evidence for Arctic invertebrates producing additional clutches in response to warming. This could be a common but overlooked phenomenon due to the challenges associated with long-term collection of life-history data in the Arctic. Moreover, given that wolf spiders are a widely distributed, important tundra predator, we may expect to see population and food web consequences of their increased reproductive rates.

SoilTemp: A global database of near-surface temperature.

Current analyses and predictions of spatially explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long-term average thermal conditions at coarse spatial resolutions only. Hence, many climate-forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing or cold-air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free-air temperatures, microclimatic ground and near-surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in. To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near-surface temperature data from all over the world. Currently, this database contains time series from 7,538 temperature sensors from 51 countries across all key biomes. The database will pave the way toward an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes.

Species-level image classification with convolutional neural network enables insect identification from habitus images.

Changes in insect biomass, abundance, and diversity are challenging to track at sufficient spatial, temporal, and taxonomic resolution. Camera traps can capture habitus images of ground-dwelling insects. However, currently sampling involves manually detecting and identifying specimens. Here, we test whether a convolutional neural network (CNN) can classify habitus images of ground beetles to species level, and estimate how correct classification relates to body size, number of species inside genera, and species identity.We created an image database of 65,841 museum specimens comprising 361 carabid beetle species from the British Isles and fine-tuned the parameters of a pretrained CNN from a training dataset. By summing up class confidence values within genus, tribe, and subfamily and setting a confidence threshold, we trade-off between classification accuracy, precision, and recall and taxonomic resolution.The CNN classified 51.9% of 19,164 test images correctly to species level and 74.9% to genus level. Average classification recall on species level was 50.7%. Applying a threshold of 0.5 increased the average classification recall to 74.6% at the expense of taxonomic resolution. Higher top value from the output layer and larger sized species were more often classified correctly, as were images of species in genera with few species.Fine-tuning enabled us to classify images with a high mean recall for the whole test dataset to species or higher taxonomic levels, however, with high variability. This indicates that some species are more difficult to identify because of properties such as their body size or the number of related species.Together, species-level image classification of arthropods from museum collections and ecological monitoring can substantially increase the amount of occurrence data that can feasibly be collected. These tools thus provide new opportunities in understanding and predicting ecological responses to environmental change.

Circumpolar terrestrial arthropod monitoring: A review of ongoing activities, opportunities and challenges, with a focus on spiders.

The terrestrial chapter of the Circumpolar Biodiversity Monitoring Programme (CBMP) has the potential to bring international multi-taxon, long-term monitoring together, but detailed fundamental species information for Arctic arthropods lags far behind that for vertebrates and plants. In this paper, we demonstrate this major challenge to the CBMP by focussing on spiders (Order: Araneae) as an example group. We collate available circumpolar data on the distribution of spiders and highlight the current monitoring opportunities and identify the key knowledge gaps to address before monitoring can become efficient. We found spider data to be more complete than data for other taxa, but still variable in quality and availability between Arctic regions, highlighting the need for greater international co-operation for baseline studies and data sharing. There is also a dearth of long-term datasets for spiders and other arthropod groups from which to assess status and trends of biodiversity. Therefore, baseline studies should be conducted at all monitoring stations and we make recommendations for the development of the CBMP in relation to terrestrial arthropods more generally.